A survey of iterative learning control

Abstract

I terative learning control (ILC) is based on the notion that the performance of a system that executes the same task multiple times can be improved by learning from previous executions (trials, iterations, passes). For instance, a basketball player shooting a free throw from a fixed position can improve his or her ability to score by practicing the shot repeatedly. During each shot, the basketball player observes the trajectory of the ball and consciously plans an alteration in the shooting motion for the next attempt. As the player continues to practice, the correct motion is learned and becomes ingrained into the muscle memory so that the shooting accuracy is iteratively improved. The converged muscle motion profile is an open-loop control generated through repetition and learning. This type of learned open-loop control strategy is the essence of ILC.We consider learning controllers for systems that perform the same operation repeatedly and under the same operating conditions. For such systems, a nonlearning con-troller yields the same tracking error on each pass. Although error signals from previous iterations are information rich, they are unused by a nonlearning controller. The objective of ILC is to improve performance by incorporating error information into the control for subsequent iterations. In doing so, high performance can be achieved with low transient tracking error despite large model uncertainty and repeating disturbances. ILC differs from other learning-type control strategies, such as adaptive control, neural networks, and repetitive control (RC). Adaptive control strategies modify the controller, which is a system, whereas ILC modifies the control input, which is a signal [1]. Additionally, adaptive controllers typically do not take advantage of the information contained in repetitive command signals. Similarly, neural network learning involves the modification of controller parameters rather than a control signal; in this case, large networks of nonlinear neurons are modified. These large networks require extensive training data, and fast convergence may be difficult to

Iterative learning control with applications in energy generation, lasers and health care

“…As detailed in, e.g. [3], (2.1) is considered in the research community to be a sufficiently general representation to include particular structures for G(q) that arise when this operator is constructed from input-output data by system identification and/or repeating disturbances and non-zero initial conditions, etc. must be modelled.…”

Learning-Based Optimal Desired Compensation Adaptive Robust Control for a Flexure-Based Micro-Motion Manipulator

“…It is noted that a low-pass filter may cause a phase shift, although the phase shift can be eliminated by filtering the signal back and forth [29]. Many types of learning functions have been developed for ILC [18], while PD-type learning function is a typical, simple and tunable ILC learning function. PD-type learning function was chosen for this paper and it is written as…”

“…Furthermore, there inevitably exists model uncertainty and perturbation. For repetitive tracking tasks, iterative learning control (ILC) [18] can be adopted to compensate for the repetitive uncertainty and disturbance by using information from previous executions. In a previous study [19], ILC was used to compensate for the hysteresis of the piezoelectric actuator while too many iteration trials had to be taken in this study to get the accurate dynamical model of the system and achieve excellent tracking performance, which is too long for real-world applications.…”

“…In a previous study [19], ILC was used to compensate for the hysteresis of the piezoelectric actuator while too many iteration trials had to be taken in this study to get the accurate dynamical model of the system and achieve excellent tracking performance, which is too long for real-world applications. In any case, ILC is sensitive to parametric uncertainty and uncertain disturbance [18]. One typical method to deal with the model uncertainty and disturbance is adaptive robust control [20][21][22], which is widely used in many industrial applications.…”

Discrete Learning Control with Application to Hydraulic Actuators

“…There has been a large number of efforts toward defining and analyzing learning control schemes, where surveys and overviews of the area may be found in Bristow et al (2006); Ahn et al (2007); Wang et al (2009) and Xu (2011). References related to the present work include Arimoto et al (1984); Craig (1984); Togai and Yamano (1985); Mita and Kato (1985); Kavli (1992); Atkeson and McIntyre (1986);Hauser (1987); Bondi et al (1988);Heinzinger et al (1989Heinzinger et al ( , 1992; Arimoto (1990); Arimoto et al (1991Arimoto et al ( , 1990; Saab et al (1993).…”